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In real time, one observation always relies on several observations. To
improve the forecasting accuracy, all these observations can be
incorporated in forecasting models. Therefore, in this study, we have
intended to introduce a new Type-2 fuzzy time series model that can utilize
more observations in forecasting. Later, this Type-2 model is enhanced by
employing particle swarm optimization (PSO) technique. The main motive
behind the utilization of the PSO with the Type-2 model is to adjust the
lengths of intervals in the universe of discourse that are employed in
forecasting, without increasing the number of intervals. The daily stock
index price data set of SBI (State Bank of India) is used to evaluate the
performance of the proposed model. The proposed model is also validated by
forecasting the daily stock index price of Google. Our experimental results
demonstrate the effectiveness and robustness of the proposed model in
comparison with existing fuzzy time series models and conventional time
series models.
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